Simone Sturniolo

TL;DR: Two open source tools to aid the processing and visualisation of ab-initio computed solid-state NMR parameters are introduced and MagresPython provides a simple scripting environment to manipulate large numbers of computed N MR parameters to search for structural correlations.

TL;DR: This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models and shows that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost.

TL;DR: In this article, the capability of using the DFT-D ab-initio random structure searching (AIRSS) method to generate crystal structures of organic molecular materials, focusing on a system (m-aminobenzoic acid; m-ABA) that is known from experimental studies to exhibit abundant polymorphism.

TL;DR: In this article, a purely theoretical method is proposed to predict muon stopping sites in crystalline materials from first principles, based on a combination of ab initio calculations, random structure searching, and machine learning.

TL;DR: A methodology, based on density functional theory and the gauge-including projector augmented wave approach, is introduced to explore the effects of packing interactions on solid-state nuclear magnetic resonance (NMR) parameters and provides insight into the development of both cluster based approaches to the predictions of chemical shifts and for empirical predictions ofchemical shifts in solids.